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In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements.
In medical imaging (e.g. for data of the same patient taken at different points in time) registration often additionally involves elastic (or nonrigid) registration to cope with elastic deformations of the body parts imaged.
Image registration algorithms fall within two realms of classification: area based methods and feature based methods. The original image is often referred to as the reference image and the image to be mapped onto the reference image is referred to as the target image. For area based image registration methods, the algorithm looks at the structure of the image via correlation metrics, Fourier properties and other means of structural analysis. However, most feature based methods, instead of looking at the overall structure of images, fine tunes its mapping to the correlation of image features: lines, curves, points, line intersections, boundaries, etc.
Further ways of classifiing an algorithm consist of the amount of data it is optimized to handle, the algorithm's application, and the central theory the algorithm is based around. Image registration has applications in remote sensing (cartography updating), medical imaging (change detection, tumor monitoring), and computer vision. Due to the vast applications image registration can be applied to, it's impossible to develop a general algorithm optimized for all uses.
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